Abstracts from the Third AACC Preanalytical Phase Conference: Implementing Preanalytical Tools That Improve Patient Care
Bibliographic record
Abstract
Clinical laboratories have numerous tools at their disposal to detect, assess, quantify, and measure error during the analytical phase of testing. Many of these tools are remarkably complex and sophisticated. For example, recent advances include precision quality control (1) and the average of patient deltas (2), both of which leverage sophisticated software and information technology solutions that improve detection of analytical error. The focus on quality management in the analytical phase is also prevalent in modern analyzers, most of which now feature advanced functions and software for detecting analytic errors such as clots, bubbles, and interfering substances (3). These advances and others have played an important role in improving the quality of reported patient results. In contrast to the analytical phase, there are far more limited options with regards to preanalytical tools available for detecting error and often a lessened focus on preanalytical error by laboratorians. Defined broadly, preanalytical error includes any error occurring prior to specimen analysis and may include test ordering, specimen collection, specimen transport, and specimen processing. Throughout this process, there are numerous opportunities for failure, including, but not limited to, mishandling specimens, mislabeling specimens, misidentifying patients, ordering the wrong test, or using the wrong collection tube. Moreover, this phase of laboratory testing most often occurs outside the confines of the laboratory’s physical location and relies heavily on non-laboratory staff. Perhaps unsurprisingly, numerous studies have demonstrated that the preanalytical phase of testing is associated with the greatest proportion of laboratory error (4). These shortcomings highlight an important need in laboratory medicine for advanced tools, awareness, and focus on the preanalytical phase of laboratory testing. The Preanalytical Phase Conference features scientific sessions and posters dedicated to the preanalytical phase of testing. This meeting provides a critical opportunity for the laboratory community to come together and highlight novel ideas, important changes, and the latest research in the area of preanalytics. The 2023 Preanalytical Phase Conference was held in Philadelphia, PA, on October 20–21, 2023. The primary theme of the conference was implementing preanalytical tools in clinical laboratories that aid in improving patient care. The objectives were for participants to learn how to integrate the preanalytical phase as team-based healthcare, streamline sample collection and transportation techniques, leverage informatics to enhance patient experience, and reduce errors and sample rejection rates. To this end, each of the speakers provided specific recommendations regarding novel tools and key takeaways for conference attendees to put into place in their laboratories. Further, the speakers and topics were as diverse as the area of preanalytics; highlights of the meeting included in-depth discussions regarding novel blood collection techniques, the future state of automation, informatics solutions to preanalytical problems, preanalytical tools for near-patient testing, and working with nursing staff to reduce errors. The online Supplemental Material features the abstracts submitted and presented during the poster session. In total, 31 abstracts were accepted from speakers across the United States, Canada, China, and the United Kingdom. The abstracts ranged in topics from novel blood collection devices, tools for capturing error, specimen ordering, transport-related error, and laboratory methods for mitigating error. In closing, the 2023 Preanalytical Phase Conference was a tremendous benefit to the laboratory community. The planning committee would like to acknowledge the efforts of all program faculty and the educational grant from BD to ADLM (formerly AACC) that made this event possible. Future meetings will be paramount to ensure progress and quality in the preanalytical phase of laboratory testing. Note: These abstracts have been reproduced without editorial alteration from the materials supplied by the authors. Infelicities of preparation, grammar, spelling, style, syntax, and usage are the authors’. #1. Improved Collection Practices for Blood Cultures Through Targeted Education of the Collection Staff in the Emergency Department #2. A “Positive” Move: Implementation of Positive Patient Identification Devices in a Large Healthcare System in Canada #3. Evaluation of a Novel Capillary Blood Collection System for Reduced Hemolysis #4. An Assessment of Individual Preference for a Novel Capillary Blood Collection System #5. Dynamic Tools to Address the Potential Financial Impact of Preanalytical Errors and Poor Specimen Quality #6. Right Test, Right Time: Reducing Inappropriate Test Orders and Improving Provider Experience Through Electronic Order Alerts #7. Use of Dried Serum and Blood Spots for Common Screening Tests on the Roche Cobas 8000 #8. Improving Meaningful Use for Body Fluid Orders #9. Impact and Frequency of IV Fluid Contamination on Basic Metabolic Panel Results Using Quality Metrics #10. Automating the Retrospective Identification of IV Fluid Contamination in Basic Metabolic Panel Results with Machine Learning #11. Delta Check Performance Assessment by Using Real-World Data at the Temple University Health System #12. Hemolysis Rates Assessed by Index Versus Test Comment: Is One Approach Better Than Another? #13. Preanalytical Phase Errors Constitute the Vast Majority of Errors in Clinical Laboratory Testing #14. Impact of Blood Collection Devices and Mode of Transportation on Peripheral Venous Blood Gas Parameters #15. Impact of Autonomous Mobile Robot on Specimen Distribution Turnaround Time and Efficiency #16. Advances in Centrifugation and Thermal Shipper Devices Enabling At-Home Liquid Blood Specimen Collection #17. Investigating the Effects of Accelerometry, Time and Temperature of Pneumatic Tube Systems on Blood Specimens #18. Benchtop Centrifugation: An Effective Method for Reducing Lipaemic Associated Interference in Grossly Lipaemic Samples? #19. The Use of Microcentrifugation in Place of Ultracentrifugation to Reduce Lipaemia in Serum/Plasma Samples #20. Effects of Hemolysis, Icterus, and Lipemia on 20 Chemistry Tests Performed in Body Fluid Specimens Measured on the Beckman Coulter AU5800 #21. An Evaluation of Contrast Media Interference on Cobas Pro Systems #22. Evaluation of Preanalytical Phase Techniques to Prepare Clotted Bloods for LC-MS/MS Analysis #23. Improved Turn-Around Time as a Result of Streamlining the PT/INR Testing #24. Performance Impacts During and After Installation of an Upgraded Preanalytical Automation System #25. Novel Portable Device for Direct-From-Blood Culture-Free Diagnosis of Bloodstream Infections #26. Impact of Specimen Type (Capillary & Venous) on Device Clinical Performance #27. Urine Analysis of Calcium, Magnesium and Phosphorus: Preanalytical Considerations for the Elimination of the Acidification Step #28. Within-Tube Stability of Selected Elements in BD Vacutainer® Trace Element K2EDTA and Serum Tubes #29. Analyzing the Durability of Assays in Body Fluids, Serum, and Urine Preserved in Different Conditions #30. The Effect of Ambient Light Exposure on Total, Conjugated, and Unconjugated Bilirubin #31. Time, Insulin Concentration, and Sample Tube Type Play Critical Roles in Hemolysis-Mediated Insulin Degradation Supplemental material is available at The Journal of Applied Laboratory Medicine online. Author Contributions: The corresponding author takes full responsibility that all authors on this publication have met the following required criteria of eligibility for authorship: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Nobody who qualifies for authorship has been omitted from the list. Christopher Farnsworth (Conceptualization-Equal, Writing—original draft-Lead), Joe El-Khoury (Conceptualization-Equal, Writing—review & editing-Equal), Amy Pyle-Eilola (Conceptualization-Equal, Writing—review & editing-Equal), and Sarah Wheeler (Conceptualization-Equal, Writing—review & editing-Equal) Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Research Funding: A.L. Pyle-Eilola, grant and payment for serving on a scientific review board from BD. Disclosures: A.L. Pyle-Eilola received honoraria from Bio-Rad and QuidelOrtho. J.M. El-Khoury received research funds from IDEXX, Bioporto, and Siemens Healthineers; consulting fees from Siemens Healthineers; and honoraria from ADLM, LabRoots; served on the Board of ADLM, and is Associate Editor for Clinical Chemistry. S. Wheeler received speaking honoraria from Siemens Healthineers and Roche and is a member of the JALM Editorial Board. C.W. Farnsworth served on the ADLM SYCL Core committee and received consulting fees from Werfen, Abbott, and Cytovale; travel support from Werfen; and unrelated research funding from Abbott, Roche, Siemens, Cepheid, Sebia, and Beckman Coulter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".